Oliver Markgraf

"Learning in reactive Synthesis"

Reactive synthesis offers an effective and promising way to solve a crucial practical problem: constructing correct and verified controllers for safety-critical systems. Rather than designing and implementing controllers by hand, reactive synthesis techniques construct controllers in an automatic fashion, thus, freeing engineers from this complex and error-prone task. In addition to being fully automatic, synthesis techniques produce correct by-construction controllers that guarantee to satisfy the given specification, or they report that no such controller exist.
In this talk I present my work on learning methods in reactive synthesis. The methods define a feedback loop, akin to counterexample-guided inductive synthesis (CEGIS). The differences between the methods are the learning process and the underlying encoding. Using different encodings allowed us to apply our methods in reactive synthesis to infinite models and models of parameterized systems.

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